LLM System Design Interview, issue 42, May 5, 2026
The Global Memory Trap
Senior AI Engineer interview at Google DeepMind, and the interviewer asks:
“Your training job is unacceptably slow, so you secure the budget to upgrade to a new cluster with 5x the raw teraFLOPs. However, your end-to-end throughput barely increases by 1.2x. What fundamental hardware scaling reality did you fail to profile before upgrading?”
Don’t say: “We must be hitting a dataloader bottleneck on the CPU side, or PCIe transfer speeds are choking the pipeline. I would optimize our asynchronous data fetching and increase the batch size to make sure we are properly saturating the new CUDA cores.”
Why treating your GPU like a pure calculator creates a hidden latency nightmare, and how kernel tiling and operator fusion keep your data trapped in ultra-fast SRAM where it belongs.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.